Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour’s angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research.
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机译:活动轮廓是视频跟踪和图像分割的非常流行的工具。由于它们的快速进化,使用参数化轮廓,并且已成为SoboLev背景中的选择方法。不幸的是,这些轮廓不容易适应拓扑变化,并且它们有时可能产生不良环,导致错误的结果。为了解决这种拓扑问题,需要一种用于轮廓的自交叉检测算法。我们通过差分拓扑的简单技术提出了一种简单的方法。通过检查给定的轮廓角度的总净变化,没有点分拣和平面扫描来实现检测。我们讨论了算法的有效实现。我们还提供用于通过角度考虑来定位交叉的算法,并通过在离散轮廓点之间绘制四个连接的线路。可以将所提出的算法添加到任何参数激活轮廓模型。我们展示了SoboLev Active Contours和所提出的算法在现实世界视频序列中成功跟踪的例子,并为进一步研究提供了想法。
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